All visualizations by default support N-dimensional image inputs. i.e., it generalizes to N-dim image inputs
to your model.

The toolkit generalizes all of the above as energy minimization problems with a clean, easy to use,
and extendable interface. Compatible with both theano and tensorflow backends with 'channels_first', 'channels_last'
data format.

In order to generate natural looking images, image search space is constrained using regularization penalties.
Some common regularizers are defined in regularizers.
Like loss functions, custom regularizer can be defined by implementing
Loss.build_loss.

Installation

Visualizations

NOTE: The links are currently broken and the entire documentation is being reworked.
Please see examples/ for samples.

Neural nets are black boxes. In the recent years, several approaches for understanding and visualizing Convolutional
Networks have been developed in the literature. They give us a way to peer into the black boxes,
diagnose mis-classifications, and assess whether the network is over/under fitting.

Guided backprop can also be used to create trippy art, neural/texture
style transfer among the list of other growing applications.

Various visualizations, documented in their own pages, are summarized here.